Semi-synthetic negatives. Besides the item indices that exist in the training set, we also consider synthesizing augmented item indices as negatives to alleviate the representation sparsity issue. However, fully-synthesized indices may be far away from ground-truth items in the sparse code representation space, which will be uninformative to guide the contrastive learning [1]}.
As a result, we consider generating semi-synthetic codes based on true item codes as hard negatives. Given a true item code \({c}_i\) , the idea is to randomly replace each index according to a Bernoulli probability parameterized by \(\rho \in (0, 1)\) , while keeping the remaining indices unchanged. In this way, the item representations derived from semi-synthetic code can be given as follows:
\(\widetilde{{v}}_i = \operatorname{Emb-Pool}\left(\operatorname{G}({c}_i)\right),\) 
